R Notebook

TODO

• Text kürzen
• Reproduce Plots
• Do a Map (Maps are alwas important!)
• PCA beschreiben

#pacman::p_install_gh("systats/binoculaR")
#devtools::install_github("strengejacke/sjlabelled",dependencies=TRUE)
#devtools::install_github("strengejacke/sjmisc",dependencies=TRUE)
#devtools::install_github("strengejacke/sjstats",dependencies=TRUE)
#devtools::install_github("strengejacke/ggeffects",dependencies=TRUE)
#devtools::install_github("strengejacke/sjPlot",dependencies=TRUE)
#devtools::install_github("lme4/lme4",dependencies=TRUE)
#pacman::p_install_gh("systats/binoculaR")
pacman::p_load(tidyverse, haven, magrittr, car, psych, sjPlot, sjstats, sjmisc, lme4, binoculaR, MuMIn)

Load in Data

arab4 <- read_spss("data/arab4.sav")
arab3 <- read_spss("data/arab3.sav")
# arab4 <- get(load(url("https://github.com/favstats/GodlyGovernance/raw/master/data/arab4.Rdata")))

Inspect Data

#b_select <- binoculaR::binoculaR(arab4)
#dput(b_select$var_codes)
#index <- c("country","wt" ,"sample" ,"a1", "q1", "q2", "q13", "q5182", 
#"q5184", "q6013", "q6014", "q6018", "q60118", "q605a", "q6061", "q6062", 
#"q6063", "q6064", "q6071", "q6074", "q6076", "q707", "q708", 
#"q7114", "q1001", "q1002", "q1003", "t1003", "q1004", "q1005", 
#"q609", "q6101", "q6106", "q1012", "q1012a", "q1016")
#
#arab4 %<>%
#  select(index)
#
#arab4
#view_df2(arab3, hide.progress = T)
# save(arab4, file = "data/arab4.Rdata")

Filter Data

arab4 %<>%
  filter(q1012 == 1) %>%#only Muslims
  mutate(sample = ifelse(is.na(sample) | sample == 1, 1, 2)) %>% 
  filter(sample != 2) #only non-refugees
arab3 %<>%
  filter(q1012 == 1) #only Muslims
arab3

arab4

Recode Data

Arab3

arab3 %<>% 
  mutate(cntry = sjmisc::to_label(country)) %>% 
  mutate(region = sjmisc::to_label(a1)) %>% 
  mutate(governorate = sjmisc::to_label(q1)) %>% 
  mutate(year = lubridate::year(date))
  # Dependent Variable
arab3 %<>% 
  mutate(islamistparties1 = ifelse(q518a2 > 5, NA, 5 - q518a2)) %>%
  mutate(islamistparties2 = ifelse(q518b2 > 5, NA, 5 - q518b2)) %>% 
  mutate(islamistparties = case_when(
        islamistparties1 == 1 ~ 1,
        islamistparties1 == 2 ~ 2,
        islamistparties1 == 3 ~ 3,
        islamistparties1 == 4 ~ 4,
        islamistparties2 == 1 ~ 1,
        islamistparties2 == 2 ~ 2,
        islamistparties2 == 3 ~ 3,
        islamistparties2 == 4 ~ 4)
    ) %>%  
  mutate(islamistgov = ifelse(q5184 > 5, NA, 5 - q5184)) %>% 
  mutate(religinterfere = ifelse(q6061 > 5, NA, q6061)) %>% 
  mutate(religleaders = ifelse(q6062 > 5, NA, 5 - q6062)) %>% 
  mutate(religleadersinfl = ifelse(q6063 > 5, NA, 5 - q6063)) %>%
  mutate(seperation = ifelse(q6064 > 5, NA, q6064)) %>% 
  mutate(religparty = Recode(q605a, "1 = 1;
                                     2 = 1;
                                     3 = 0;
                                     4 = 0;
                                     5 = 0;
                                    8 = 0;
                                    9 = NA")) %>% 
  mutate(religparty2 = Recode(q605a, "1 = 5;
                                      2 = 4;
                                      3 = 2;
                                      4 = 1;
                                      5 = 3;
                                     8 = NA;
                                     9 = NA"))
ifelse4cat_rec <- function(variable) {
  recoded <- ifelse(variable == 0 | variable > 5, NA, 5 - variable)
  return(recoded)
}
arab3 %<>% 
  mutate(female = ifelse(sex == 2, 1, 0)) %>% 
  mutate(work = ifelse(q1004 == 0 | q1004 > 5, NA, abs(q1004 - 2))) %>% 
  mutate(income = ifelse4cat_rec(q1016)) %>% 
  mutate(age = ifelse(q1001 == 0 | q1001 == 9999, NA, q1001)) %>% 
  mutate(educ = case_when(
        q1003 == 0 ~ NA_real_,
        q1003 == 99 ~ NA_real_,
        q1003 == 5 ~ 4,
        q1003 == 6 ~ 5,
        q1003 == 7 ~ 6,
        q1003yem == 0 ~ NA_real_,
        q1003yem == 99 ~ NA_real_,
        q1003yem == 4 ~ 3,
        q1003yem == 5 ~ 4,
        q1003yem == 6 ~ 5,
        q1003yem == 7 ~ 5,
        q1003yem == 8 ~ 6,
        q1003t == 0 ~ NA_real_,
        q1003t == 99 ~ NA_real_,
        q1003t == 1 ~ 1,
        q1003t == 2 ~ 2,
        q1003t == 3 ~ 3,
        q1003t == 4 ~ 4,
        q1003t == 5 ~ 5,
        q1003t == 6 ~ 6,
    TRUE ~ as.numeric(q1003))
    ) %>% 
  mutate(globalism = ifelse(q701b == 0 | q701b > 5, NA, q701b)) %>% 
  mutate(pray = ifelse(q6101 == 0 | q6101 > 5, NA, 6 - q6101)) %>% 
  mutate(quran = ifelse(q6106 == 0 | q6106 > 5, NA, 6 - q6106)) %>%
  mutate(womanwork = ifelse(q6012 == 0 | q6012 > 5, NA, q6012)) %>% 
  mutate(womenleader = ifelse4cat_rec(q6013)) %>% 
  mutate(womeneduc = ifelse4cat_rec(q6014)) %>% 
  mutate(nodemoc = ifelse(q6071 == 0 | q6071 > 5, NA, q6071)) %>% 
  mutate(genderapartuni = ifelse4cat_rec(q6074)) %>% 
  mutate(coverup = ifelse4cat_rec(q6076)) %>% 
  select(cntry, year, region, governorate, islamistparties , islamistgov, religinterfere, religleaders, religleadersinfl, seperation, religparty, religparty2, female, work, income,  age, educ, globalism, pray, quran, womanwork, womenleader, womeneduc, nodemoc, genderapartuni, coverup)

Arab4

table(arab4$country)

   1    5    8   10   13   15   21 
1199 1146 1172  615 1199 1177 1200 

arab4 %<>% 
  mutate(cntry = sjmisc::to_label(country)) %>% 
  mutate(region = sjmisc::to_label(a1)) %>% 
  mutate(governorate = sjmisc::to_label(q1)) %>% 
  mutate(year = 2016)#%>%
#  mutate(district = sjmisc::to_label(q2)) 
# Dependent Variable
arab4 %<>% 
  mutate(islamistparties = ifelse(q5182 > 5, NA, 5 - q5182)) %>% 
  mutate(islamistgov = ifelse(q5184 > 5, NA, 5 - q5184)) %>% 
  mutate(religinterfere = ifelse(q6061 > 5, NA, q6061)) %>% 
  mutate(religleaders = ifelse(q6062 > 5, NA, 5 - q6062)) %>% 
  mutate(religleadersinfl = ifelse(q6063 > 5, NA, 5 - q6063)) %>%
  mutate(seperation = ifelse(q6064 > 5, NA, q6064)) %>% 
  mutate(religparty = Recode(q605a, "1 = 1;
                                     2 = 1;
                                     3 = 0;
                                     4 = 0;
                                     5 = 0;
                                    98 = 0;
                                    99 = NA")) %>% 
  mutate(religparty2 = Recode(q605a, "1 = 5;
                                      2 = 4;
                                      3 = 2;
                                      4 = 1;
                                      5 = 3;
                                     98 = NA;
                                     99 = NA"))
arab4 %<>% 
  mutate(female = ifelse(q1002 == 2, 1, 0)) %>% 
  mutate(work = ifelse(q1004 == 0 | q1004 > 5, NA, abs(q1004 - 2))) %>% 
  mutate(income = ifelse4cat_rec(q1016)) %>% 
  mutate(age = ifelse(q1001 == 0 | q1001 == 9999, NA, q1001)) %>% 
  mutate(educ = case_when(
        q1003 == 0 ~ NA_real_,
        q1003 == 98 ~ NA_real_,
        q1003 == 99 ~ NA_real_,
        q1003 == 5 ~ 4,
        q1003 == 6 ~ 5,
        q1003 == 7 ~ 6,
        t1003 == 0 ~ NA_real_,
        t1003 == 98 ~ NA_real_,
        t1003 == 99 ~ NA_real_,
        t1003 == 3 ~ 3,
        t1003 == 4 ~ 4,
        t1003 == 5 ~ 5,
        t1003 == 6 ~ 6,
    TRUE ~ as.numeric(q1003))
    ) %>% 
  mutate(globalism = ifelse(q701b == 0 | q701b > 5, NA, q701b)) %>% 
  mutate(pray = ifelse(q6101 == 0 | q6101 > 5, NA, 6 - q6101)) %>% 
  mutate(quran = ifelse(q6106 == 0 | q6106 > 5, NA, 6 - q6106)) %>% 
  mutate(womanwork = ifelse(q6012 == 0 | q6012 > 5, NA, q6012)) %>% 
  mutate(womenleader = ifelse4cat_rec(q6013)) %>% 
  mutate(womeneduc = ifelse4cat_rec(q6014)) %>% 
  mutate(nodemoc = ifelse(q6071 == 0 | q6071 > 5, NA, q6071)) %>% 
  mutate(genderapartuni = ifelse4cat_rec(q6074)) %>% 
  mutate(coverup = ifelse4cat_rec(q6076)) %>% 
  select(cntry, year, region, governorate , islamistparties , islamistgov, religinterfere, religleaders, religleadersinfl, seperation, religparty, religparty2, female, work, income,  age, educ, globalism, pray, quran, womanwork, womenleader, womeneduc, nodemoc, genderapartuni, coverup)

Merging

Factor Analysis

# f1 <- arab %>% 
#   select(islamistparties, islamistgov, 
#          religleaders, religleadersinfl) %>%
#   # na.omit() %>% 
#   psych::fa(1, rotate = "varimax",   
#         fm = "pa",
#         scores = "regression",
#         weight = na.omit(arab$wt)  )         
# fa.diagram(f1)                                  
# f1              
f2 <- arab %>% 
  select(islamistparties, islamistgov, 
         religleaders, religleadersinfl) %>% 
  psych::pca(weight = arab$wt) 
arab %>% 
  select(islamistparties, islamistgov, 
         religleaders, religleadersinfl) %>% 
  alpha() 

Reliability analysis   
Call: alpha(x = .)

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd
       0.7       0.7    0.66      0.37 2.3 0.0034    2 0.71

 lower alpha upper     95% confidence boundaries
0.69 0.7 0.71 

 Reliability if an item is dropped:
                 raw_alpha std.alpha G6(smc) average_r S/N alpha se
islamistparties       0.65      0.65    0.58      0.39 1.9   0.0042
islamistgov           0.65      0.66    0.58      0.39 1.9   0.0041
religleaders          0.60      0.60    0.51      0.33 1.5   0.0046
religleadersinfl      0.64      0.64    0.54      0.37 1.7   0.0042

 Item statistics 
                     n raw.r std.r r.cor r.drop mean   sd
islamistparties  20198  0.72  0.71  0.55   0.46  1.7 0.95
islamistgov      20135  0.73  0.71  0.54   0.46  1.7 1.02
religleaders     20431  0.75  0.76  0.66   0.53  2.3 0.92
religleadersinfl 20524  0.72  0.73  0.60   0.48  2.2 0.89

Non missing response frequency for each item
                    1    2    3    4 5 miss
islamistparties  0.59 0.19 0.15 0.07 0 0.08
islamistgov      0.59 0.17 0.14 0.10 0 0.08
religleaders     0.21 0.38 0.31 0.10 0 0.07
religleadersinfl 0.24 0.42 0.26 0.08 0 0.06

arab %>% 
  select(islamistparties, islamistgov, 
         religleaders, religleadersinfl) %>% 
  KMO() 

Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = .)
Overall MSA =  0.68
MSA for each item = 
 islamistparties      islamistgov     religleaders religleadersinfl 
            0.71             0.72             0.66             0.66 

arab4 %>% 
  select(islamistparties, islamistgov, 
         religleaders, religleadersinfl,
         religparty2, seperation,
         religinterfere) %>% 
  na.omit() %>% 
  cor()  

                 islamistparties islamistgov religleaders religleadersinfl
islamistparties        1.0000000   0.3760955    0.3274069        0.2595854
islamistgov            0.3760955   1.0000000    0.3180997        0.3059820
religleaders           0.3274069   0.3180997    1.0000000        0.5628358
religleadersinfl       0.2595854   0.3059820    0.5628358        1.0000000
religparty2            0.2625742   0.2409189    0.3812004        0.3120300
seperation             0.1409401   0.2186956    0.3190967        0.2944263
religinterfere         0.1393481   0.1283822    0.2070613        0.2539891
                 religparty2 seperation religinterfere
islamistparties    0.2625742  0.1409401      0.1393481
islamistgov        0.2409189  0.2186956      0.1283822
religleaders       0.3812004  0.3190967      0.2070613
religleadersinfl   0.3120300  0.2944263      0.2539891
religparty2        1.0000000  0.2819491      0.1671423
seperation         0.2819491  1.0000000      0.2725233
religinterfere     0.1671423  0.2725233      1.0000000

# arab <- predict.psych(f1, arab %>% 
#   select(islamistparties, islamistgov, 
#          religleaders, religleadersinfl)) %>% 
#   tbl_df() %>% 
#   cbind(arab) %>% 
#   mutate(islamism_fa = PA1)
arab <- predict.psych(f2, arab %>% 
  select(islamistparties, islamistgov, 
         religleaders, religleadersinfl)) %>% 
  tbl_df() %>% 
  transmute(islamism = PC1) %>% 
  cbind(arab) 
#%>% 
#  ggplot(aes(islamism)) +
#  geom_histogram() +
#  facet_wrap(~cntry, scales = "free")
#sjPlot::view_df(arab, show.frq = T, show.prc = T)
# 
# cor.test(arab$PC1, arab$PA1)

Creating indices

Regression

model1 <- lme4::lmer(islamism ~ female + work + income + age + educ + globalism + antiwestern + personalpiety + patriarchalvalues + liberalislam + year_2012 + year_2013 + year_2014 + (1|cntry), data = arab_reg, weights = wt)
texreg::screenreg(model1)

=====================================
                        Model 1      
-------------------------------------
(Intercept)                 -0.18    
                            (0.11)   
female                       0.07 ***
                            (0.02)   
work                        -0.06 ***
                            (0.02)   
income                       0.00    
                            (0.01)   
age                         -0.00 ***
                            (0.00)   
educ                        -0.03 ***
                            (0.01)   
globalism                    0.08 ***
                            (0.01)   
antiwestern                 -0.06 *  
                            (0.03)   
personalpiety                0.54 ***
                            (0.04)   
patriarchalvalues            0.76 ***
                            (0.04)   
liberalislam                -1.04 ***
                            (0.04)   
year_2012                    0.00    
                            (0.04)   
year_2013                    0.18 ***
                            (0.02)   
year_2014                   -0.08    
                            (0.09)   
-------------------------------------
AIC                      33078.64    
BIC                      33197.69    
Log Likelihood          -16523.32    
Num. obs.                12591       
Num. groups: cntry          12       
Var: cntry (Intercept)       0.10    
Var: Residual                0.69    
=====================================
*** p < 0.001, ** p < 0.01, * p < 0.05

model1 <- arab_reg %>% 
  lme4::lmer(islamism ~ female + work + income + age + educ + globalism + pray + quran + womanwork + womenleader + womeneduc + nodemoc + genderapartuni + coverup + (1|cntry/year), data = .)
texreg::screenreg(model1)

==========================================
                             Model 1      
------------------------------------------
(Intercept)                      33.38 ***
                                 (2.54)   
female                            1.89 ***
                                 (0.38)   
work                             -1.31 ***
                                 (0.38)   
income                            0.28    
                                 (0.18)   
age                              -1.02 ***
                                 (0.19)   
educ                             -0.74 ***
                                 (0.20)   
globalism                         1.63 ***
                                 (0.18)   
pray                              1.31 ***
                                 (0.20)   
quran                             2.05 ***
                                 (0.19)   
womanwork                         0.78 ***
                                 (0.22)   
womenleader                       2.40 ***
                                 (0.18)   
womeneduc                         2.14 ***
                                 (0.20)   
nodemoc                          -3.63 ***
                                 (0.21)   
genderapartuni                   -4.56 ***
                                 (0.22)   
coverup                          -0.82 ***
                                 (0.18)   
------------------------------------------
AIC                          118239.29    
BIC                          118374.48    
Log Likelihood               -59101.64    
Num. obs.                     13499       
Num. groups: year:cntry          19       
Num. groups: cntry               12       
Var: year:cntry (Intercept)      14.12    
Var: cntry (Intercept)           37.00    
Var: Residual                   369.66    
==========================================
*** p < 0.001, ** p < 0.01, * p < 0.05

plot_model(model1, sort.est = T, show.values = T, show.p = T, value.offset = 0.5)

Computing p-values via Wald-statistics approximation (treating t as Wald z).

plot_model(model1, terms = c("nodemoc", "female"), type = "pred")

Note: uncertainty of the random effects parameters are not taken into account for confidence intervals.

model2 <- arab_reg %>% 
  lme4::lmer(islamism ~ female + work + income + age + educ + globalism + pray + quran + womanwork + womenleader + womeneduc + nodemoc + genderapartuni + coverup + nodemoc*quran + (1|cntry/year), data = .)
texreg::screenreg(model2)

==========================================
                             Model 1      
------------------------------------------
(Intercept)                      35.34 ***
                                 (3.22)   
female                            1.89 ***
                                 (0.38)   
work                             -1.31 ***
                                 (0.38)   
income                            0.28    
                                 (0.18)   
age                              -1.02 ***
                                 (0.19)   
educ                             -0.74 ***
                                 (0.20)   
globalism                         1.63 ***
                                 (0.18)   
pray                              1.32 ***
                                 (0.20)   
quran                             1.53 ** 
                                 (0.56)   
womanwork                         0.79 ***
                                 (0.22)   
womenleader                       2.40 ***
                                 (0.18)   
womeneduc                         2.14 ***
                                 (0.20)   
nodemoc                          -4.32 ***
                                 (0.72)   
genderapartuni                   -4.55 ***
                                 (0.22)   
coverup                          -0.82 ***
                                 (0.18)   
quran:nodemoc                     0.18    
                                 (0.18)   
------------------------------------------
AIC                          118241.91    
BIC                          118384.60    
Log Likelihood               -59101.95    
Num. obs.                     13499       
Num. groups: year:cntry          19       
Num. groups: cntry               12       
Var: year:cntry (Intercept)      14.13    
Var: cntry (Intercept)           37.02    
Var: Residual                   369.66    
==========================================
*** p < 0.001, ** p < 0.01, * p < 0.05

view_df2 <- function (x, weight.by = NULL, altr.row.col = TRUE, show.id = TRUE, 
  show.type = FALSE, show.values = TRUE, show.string.values = FALSE, 
  show.labels = TRUE, show.frq = FALSE, show.prc = FALSE, 
  show.wtd.frq = FALSE, show.wtd.prc = FALSE, show.na = FALSE, 
  max.len = 15, sort.by.name = FALSE, wrap.labels = 50, hide.progress = FALSE, 
  CSS = NULL, encoding = NULL, file = NULL, use.viewer = TRUE, 
  no.output = FALSE, remove.spaces = TRUE) 
{
  get.encoding <- function(encoding, data = NULL) {
  if (is.null(encoding)) {
    if (!is.null(data) && is.data.frame(data)) {
      # get variable label
      labs <- sjlabelled::get_label(data[[1]])
      # check if vectors of data frame have
      # any valid label. else, default to utf-8
      if (!is.null(labs) && is.character(labs))
        encoding <- Encoding(sjlabelled::get_label(data[[1]]))
      else
        encoding <- "UTF-8"
      # unknown encoding? default to utf-8
      if (encoding == "unknown") encoding <- "UTF-8"
    } else if (.Platform$OS.type == "unix")
      encoding <- "UTF-8"
    else
      encoding <- "Windows-1252"
  }
  return(encoding)
}

  has_value_labels <- function(x) {
  !(is.null(attr(x, "labels", exact = T)) && is.null(attr(x, "value.labels", exact = T)))
}

  sju.rmspc <- function(html.table) {
  cleaned <- gsub("      <", "<", html.table, fixed = TRUE, useBytes = TRUE)
  cleaned <- gsub("    <", "<", cleaned, fixed = TRUE, useBytes = TRUE)
  cleaned <- gsub("  <", "<", cleaned, fixed = TRUE, useBytes = TRUE)
  return(cleaned)
  }
  
  frq.value <- function(index, x, df.val, weights = NULL) {
  valstring <- ""
  # check if we have a valid index
  if (index <= ncol(x) && !is.null(df.val[[index]])) {
    # do we have weights?
    if (!is.null(weights))
      variab <- sjstats::weight(x[[index]], weights)
    else
      variab <- x[[index]]
    # create frequency table. same function as for
    # sjt.frq and sjp.frq
    ftab <- create.frq.df(variab, 20)$mydat$frq
    # remove last value, which is N for NA
    if (length(ftab) == 1 && is.na(ftab)) {
      valstring <- "<NA>"
    } else {
      for (i in 1:(length(ftab) - 1)) {
        valstring <- paste0(valstring, ftab[i])
        if (i < length(ftab)) valstring <- paste0(valstring, "<br>")
      }
    }
  } else {
    valstring <- ""
  }
  return(valstring)
}

prc.value <- function(index, x, df.val, weights = NULL) {
  valstring <- ""
  # check for valid indices
  if (index <= ncol(x) && !is.null(df.val[[index]])) {
    # do we have weights?
    if (!is.null(weights))
      variab <- sjstats::weight(x[[index]], weights)
    else
      variab <- x[[index]]
    # create frequency table, but only get valid percentages
    ftab <- create.frq.df(variab, 20)$mydat$valid.prc
    # remove last value, which is a NA dummy
    if (length(ftab) == 1 && is.na(ftab)) {
      valstring <- "<NA>"
    } else {
      for (i in 1:(length(ftab) - 1)) {
        valstring <- paste0(valstring, sprintf("%.2f", ftab[i]))
        if (i < length(ftab)) valstring <- paste0(valstring, "<br>")
      }
    }
  } else {
    valstring <- ""
  }
  return(valstring)
}
  
  encoding <- get.encoding(encoding, x)
  if (!is.data.frame(x)) 
    stop("Parameter needs to be a data frame!", call. = FALSE)
  df.var <- sjlabelled::get_label(x)
  df.val <- sjlabelled::get_labels(x)
  colcnt <- ncol(x)
  id <- seq_len(colcnt)
  if (sort.by.name) 
    id <- id[order(colnames(x))]
  tag.table <- "table"
  tag.thead <- "thead"
  tag.tdata <- "tdata"
  tag.arc <- "arc"
  tag.caption <- "caption"
  tag.omit <- "omit"
  css.table <- "border-collapse:collapse; border:none;"
  css.thead <- "border-bottom:double; font-style:italic; font-weight:normal; padding:0.2cm; text-align:left; vertical-align:top;"
  css.tdata <- "padding:0.2cm; text-align:left; vertical-align:top;"
  css.arc <- "background-color:#eeeeee"
  css.caption <- "font-weight: bold; text-align:left;"
  css.omit <- "color:#999999;"
  if (!is.null(CSS)) {
    if (!is.null(CSS[["css.table"]])) 
      css.table <- ifelse(substring(CSS[["css.table"]], 
        1, 1) == "+", paste0(css.table, substring(CSS[["css.table"]], 
        2)), CSS[["css.table"]])
    if (!is.null(CSS[["css.thead"]])) 
      css.thead <- ifelse(substring(CSS[["css.thead"]], 
        1, 1) == "+", paste0(css.thead, substring(CSS[["css.thead"]], 
        2)), CSS[["css.thead"]])
    if (!is.null(CSS[["css.tdata"]])) 
      css.tdata <- ifelse(substring(CSS[["css.tdata"]], 
        1, 1) == "+", paste0(css.tdata, substring(CSS[["css.tdata"]], 
        2)), CSS[["css.tdata"]])
    if (!is.null(CSS[["css.arc"]])) 
      css.arc <- ifelse(substring(CSS[["css.arc"]], 1, 
        1) == "+", paste0(css.arc, substring(CSS[["css.arc"]], 
        2)), CSS[["css.arc"]])
    if (!is.null(CSS[["css.caption"]])) 
      css.caption <- ifelse(substring(CSS[["css.caption"]], 
        1, 1) == "+", paste0(css.caption, substring(CSS[["css.caption"]], 
        2)), CSS[["css.caption"]])
    if (!is.null(CSS[["css.omit"]])) 
      css.omit <- ifelse(substring(CSS[["css.omit"]], 
        1, 1) == "+", paste0(css.omit, substring(CSS[["css.omit"]], 
        2)), CSS[["css.omit"]])
  }
  page.style <- sprintf("<style>\nhtml, body { background-color: white; }\n%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n%s { %s }\n.%s { %s }\n</style>", 
    tag.table, css.table, tag.thead, css.thead, tag.tdata, 
    css.tdata, tag.arc, css.arc, tag.caption, css.caption, 
    tag.omit, css.omit)
  toWrite <- sprintf("<html>\n<head>\n<meta http-equiv=\"Content-type\" content=\"text/html;charset=%s\">\n%s\n</head>\n<body>\n", 
    encoding, page.style)
  page.content <- sprintf("<table>\n  <caption>Data frame: %s</caption>\n", 
    deparse(substitute(x)))
  page.content <- paste0(page.content, "  <tr>\n    ")
  if (show.id) 
    page.content <- paste0(page.content, "<th class=\"thead\">ID</th>")
  page.content <- paste0(page.content, "<th class=\"thead\">Name</th>")
  if (show.type) 
    page.content <- paste0(page.content, "<th class=\"thead\">Type</th>")
  page.content <- paste0(page.content, "<th class=\"thead\">Label</th>")
  if (show.na) 
    page.content <- paste0(page.content, "<th class=\"thead\">missings</th>")
  if (show.values) 
    page.content <- paste0(page.content, "<th class=\"thead\">Values</th>")
  if (show.labels) 
    page.content <- paste0(page.content, "<th class=\"thead\">Value Labels</th>")
  if (show.frq) 
    page.content <- paste0(page.content, "<th class=\"thead\">Freq.</th>")
  if (show.prc) 
    page.content <- paste0(page.content, "<th class=\"thead\">%</th>")
  if (show.wtd.frq) 
    page.content <- paste0(page.content, "<th class=\"thead\">weighted Freq.</th>")
  if (show.wtd.prc) 
    page.content <- paste0(page.content, "<th class=\"thead\">weighted %</th>")
  page.content <- paste0(page.content, "\n  </tr>\n")
  if (!hide.progress) 
    pb <- utils::txtProgressBar(min = 0, max = colcnt, style = 3)
  for (ccnt in seq_len(colcnt)) {
    index <- id[ccnt]
    arcstring <- ""
    if (altr.row.col) 
      arcstring <- ifelse(sjmisc::is_even(ccnt), " arc", 
        "")
    page.content <- paste0(page.content, "  <tr>\n")
    if (show.id) 
      page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%i</td>\n", 
        arcstring, index))
    if (!is.list(x[[index]]) && !is.null(sjlabelled::get_note(x[[index]]))) 
      td.title.tag <- sprintf(" title=\"%s\"", sjlabelled::get_note(x[[index]]))
    else td.title.tag <- ""
    page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\"%s>%s</td>\n", 
      arcstring, td.title.tag, colnames(x)[index]))
    if (show.type) {
      vartype <- sjmisc::var_type(x[[index]])
      page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
        arcstring, vartype))
    }
    if (index <= length(df.var)) {
      varlab <- df.var[index]
      if (!is.null(wrap.labels)) {
        varlab <- sjmisc::word_wrap(varlab, wrap.labels, 
          "<br>")
      }
    }
    else {
      varlab <- "<NA>"
    }
    page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
      arcstring, varlab))
    if (show.na) {
      if (is.list(x[[index]])) {
        page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\"><span class=\"omit\">&lt;list&gt;</span></td>\n", 
          arcstring))
      }
      else {
        page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%i (%.2f%%)</td>\n", 
          arcstring, sum(is.na(x[[index]]), na.rm = T), 
          100 * sum(is.na(x[[index]]), na.rm = T)/nrow(x)))
      }
    }
    if (is.numeric(x[[index]]) && !has_value_labels(x[[index]])) {
      if (show.values || show.labels) {
        valstring <- paste0(sprintf("%a", range(x[[index]], 
          na.rm = T)), collapse = "-")
        if (show.values && show.labels) {
          colsp <- " colspan=\"2\""
          valstring <- paste0("<em>range: ", valstring, 
            "</em>")
        }
        else {
          colsp <- ""
        }
        page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\"%s>%s</td>\n", 
          arcstring, colsp, valstring))
      }
    }
    else {
      if (show.values) {
        valstring <- ""
        if (index <= ncol(x)) {
          if (is.list(x[[index]])) {
            valstring <- "<span class=\"omit\">&lt;list&gt;</span>"
          }
          else {
            vals <- sjlabelled::get_values(x[[index]])
            if (!is.null(vals)) {
              loop <- na.omit(seq_len(length(vals))[1:max.len])
              for (i in loop) {
                valstring <- paste0(valstring, vals[i])
                if (i < length(vals)) 
                  valstring <- paste0(valstring, "<br>")
              }
              if (max.len < length(vals)) 
                valstring <- paste0(valstring, "<span class=\"omit\">&lt;...&gt;</span>")
            }
          }
        }
        else {
          valstring <- "<NA>"
        }
        page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
          arcstring, valstring))
      }
      if (show.labels) {
        valstring <- ""
        if (index <= length(df.val)) {
          if (is.list(x[[index]])) {
            valstring <- "<span class=\"omit\">&lt;list&gt;</span>"
          }
          else {
            vals <- df.val[[index]]
            if (!is.null(vals)) 
              vals <- na.omit(vals)
            if (is.character(x[[index]]) && !is.null(vals) && 
              !sjmisc::is_empty(vals)) {
              if (show.string.values) 
                vals <- sort(vals)
              else vals <- "<span class=\"omit\" title =\"'show.string.values = TRUE' to show values.\">&lt;output omitted&gt;</span>"
            }
            if (!is.null(vals)) {
              loop <- na.omit(seq_len(length(vals))[1:max.len])
              for (i in loop) {
                valstring <- paste0(valstring, vals[i])
                if (i < length(vals)) 
                  valstring <- paste0(valstring, "<br>")
              }
              if (max.len < length(vals)) 
                valstring <- paste0(valstring, "<span class=\"omit\">&lt;... truncated&gt;</span>")
            }
          }
        }
        else {
          valstring <- "<NA>"
        }
        page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
          arcstring, valstring))
      }
    }
    if (show.frq) {
      if (is.list(x[[index]])) 
        valstring <- "<span class=\"omit\">&lt;list&gt;</span>"
      else valstring <- frq.value(index, x, df.val)
      page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
        arcstring, valstring))
    }
    if (show.prc) {
      if (is.list(x[[index]])) 
        valstring <- "<span class=\"omit\">&lt;list&gt;</span>"
      else valstring <- prc.value(index, x, df.val)
      page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
        arcstring, valstring))
    }
    if (show.wtd.frq && !is.null(weight.by)) {
      if (is.list(x[[index]])) 
        valstring <- "<span class=\"omit\">&lt;list&gt;</span>"
      else valstring <- frq.value(index, x, df.val, weight.by)
      page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
        arcstring, valstring))
    }
    if (show.prc && !is.null(weight.by)) {
      if (is.list(x[[index]])) 
        valstring <- "<span class=\"omit\">&lt;list&gt;</span>"
      else valstring <- prc.value(index, x, df.val, weight.by)
      page.content <- paste0(page.content, sprintf("    <td class=\"tdata%s\">%s</td>\n", 
        arcstring, valstring))
    }
    if (!hide.progress) 
      utils::setTxtProgressBar(pb, ccnt)
    page.content <- paste0(page.content, "  </tr>\n")
  }
  if (!hide.progress) 
    close(pb)
  page.content <- paste(page.content, "</table>", sep = "\n")
  toWrite <- paste0(toWrite, sprintf("%s\n</body></html>", 
    page.content))
  knitr <- page.content
  knitr <- gsub("class=", "style=", knitr, fixed = TRUE, useBytes = TRUE)
  knitr <- gsub("<table", sprintf("<table style=\"%s\"", css.table), 
    knitr, fixed = TRUE, useBytes = TRUE)
  knitr <- gsub(tag.tdata, css.tdata, knitr, fixed = TRUE, 
    useBytes = TRUE)
  knitr <- gsub(tag.thead, css.thead, knitr, fixed = TRUE, 
    useBytes = TRUE)
  knitr <- gsub(tag.arc, css.arc, knitr, fixed = TRUE, useBytes = TRUE)
  if (remove.spaces) {
    knitr <- sju.rmspc(knitr)
    toWrite <- sju.rmspc(toWrite)
    page.content <- sju.rmspc(page.content)
  }
  structure(class = c("sjTable", "view_df"), list(page.style = page.style, 
    page.content = page.content, output.complete = toWrite, 
    header = NULL, knitr = knitr, file = file, show = !no.output, 
    use.viewer = use.viewer))
}

view_df2(arab4, hide.progress = T)